Compare YOLOv8 vs YOLOX live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
YOLOv8 vs YOLOX Comparison Table
| Property | YOLOv8 | YOLOX |
|---|---|---|
| Organization | Ultralytics | Megvii |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Jan 2023 | Jul 2021 |
| Context Window | — | — |
| Parameters | 3.2M-68.2M | 0.91M-99.1M |
| License | AGPL 3.0 | Apache 2.0 |
| Commercial use | commercial use needs a license (included on Roboflow cloud) | free for commercial use |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | 52.9XL · 68.2M params | Not benchmarked |
| Smallest size | 36.5Nano · 3.2M params | Not benchmarked |
| mAP range | 36.5–52.9across 5 sizes | Not benchmarked |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | 36.5640×640 · 3.2M params | Not available |
| Small | 44.1640×640 · 11.2M params | Not available |
| Medium | 49.2640×640 · 25.9M params | Not available |
| Large | 51.8640×640 · 43.7M params | Not available |
| XL | 52.9640×640 · 68.2M params | Not available |
| COCO val2017 mAP 50:95 and parameter counts from leaderboard.roboflow.com. Size names don't line up across model families, so compare sizes with similar parameter counts. The live demo runs some of these sizes at other resolutions, which were not benchmarked. | ||
| Vision Tasks | ||
| Object Detection | Demo (COCO) | Supported |
| Model Features | ||
| Real-Time Vision | Supported | Not listed |
YOLOv8 vs YOLOX: Overview
YOLOv8 is an object detection and multi-task vision model developed by Ultralytics, released in January 2023 under the AGPL-3.0 license. It succeeds YOLOv5 and introduces an anchor-free detection head, a new C2f module for improved gradient flow, and a decoupled head that separates classification and regression tasks. These changes improve both accuracy and training efficiency compared to earlier Ultralytics models.
YOLOv8 supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a unified codebase. It is available in five sizes from Nano to Extra Large and exports to ONNX, TensorRT, CoreML, and other formats. YOLOv8 is one of the most widely adopted detection models in production and is directly supported by Roboflow Inference for custom model training and deployment.
YOLOX is an anchor-free object detection model developed by Megvii (Face++), released in July 2021 under the Apache 2.0 license. It applies anchor-free detection to the YOLO framework, decoupling the classification and regression heads to allow each to optimize independently, and introduces the SimOTA label assignment strategy for improved training convergence. YOLOX achieves strong accuracy-speed tradeoffs and outperforms YOLOv5 on COCO at comparable model sizes.
YOLOX-L achieves 50.0% AP on COCO at 68.9 FPS on an NVIDIA V100 GPU. The model is available in a range of sizes from YOLOX-Nano to YOLOX-X and supports deployment through ONNX, TensorRT, and other standard export formats. It is suitable for real-time object detection applications and has been widely adopted in industrial and research detection pipelines.
